| # COPGPT - RAG-based Policy Recommendation Chatbot | |
| A sophisticated Retrieval-Augmented Generation (RAG) chatbot built with FastAPI, LangChain, and OpenAI, designed to provide intelligent policy recommendations and sustainability insights with a focus on environmental topics and carbon emissions. | |
| ## Features | |
| - **Hybrid Search Architecture**: Combines FAISS vector search with Google Search fallback for comprehensive information retrieval | |
| - **Conversational Memory**: Maintains context across multiple interactions for coherent conversations | |
| - **Document Processing**: Supports multiple file formats (PDF, DOCX, TXT, CSV, XLSX, HTML, MD, PPT) | |
| - **Metadata Preservation**: Retains source information and references for all retrieved documents | |
| - **Real-time Web Search**: Falls back to Google Search when local knowledge base lacks information | |
| - **Clean Response Formatting**: Provides well-structured responses with proper references | |
| - **FastAPI Backend**: High-performance asynchronous API endpoints | |
| ## Architecture | |
| ``` | |
| ┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐ | |
| │ Web Interface │────▶│ FastAPI Server │────▶│ RAG Pipeline │ | |
| └─────────────────┘ └──────────────────┘ └─────────────────┘ | |
| │ | |
| ┌──────────────────────────┴───────────────────────────┐ | |
| │ │ | |
| ┌───────▼────────┐ ┌────────▼────────┐ | |
| │ FAISS Vector │ │ Google Search │ | |
| │ Store │ │ (Fallback) │ | |
| └────────────────┘ └─────────────────┘ | |
| ``` | |
| ## Github code | |
| https://github.com/Nelsonchris1/Accelerating-Policy-Decisions---Research/tree/main | |
| ## Prerequisites | |
| - Python 3.8+ | |
| - OpenAI API Key | |
| - Google Serper API Key (for web search functionality) | |
| - FAISS-compatible system | |
| ## Installation | |
| 1. **Clone the repository** | |
| ```bash | |
| git clone <repository-url> | |
| cd COP29_RAG_Chatbot | |
| ``` | |
| 2. **Create a virtual environment** | |
| ```bash | |
| python -m venv venv | |
| source venv/bin/activate # On Windows: venv\Scripts\activate | |
| ``` | |
| 3. **Install dependencies** | |
| ```bash | |
| pip install -r requirements.txt | |
| ``` | |
| 4. **Set up environment variables** | |
| Create a `.env` file in the root directory: | |
| ```env | |
| OPENAI_API_KEY=your_openai_api_key_here | |
| SERPER_API_KEY=your_serper_api_key_here | |
| ``` | |
| ## Project Structure | |
| ``` | |
| COP29_RAG_Chatbot/ | |
| │ | |
| ├── app.py # FastAPI application entry point | |
| ├── retriever.py # Main RAG pipeline and chat logic | |
| ├── embeddings.py # Document embedding and vector store management | |
| ├── file_loader.py # Multi-format document loader | |
| ├── metadata.py # Metadata inspection utilities | |
| ├── requirements.txt # Python dependencies | |
| │ | |
| ├── models/ # Data models | |
| │ └── index.py # Chat model definitions | |
| │ | |
| ├── templates/ # HTML templates | |
| │ └── index.html # Chat interface | |
| │ | |
| ├── static/ # Static assets (CSS, JS, images) | |
| │ | |
| └── test2_db/ # FAISS vector database storage | |
| └── document_chunks111/ | |
| ``` | |
| ## Configuration | |
| ### Vector Database Setup | |
| 1. **Prepare your documents** | |
| Place your documents in a folder for processing. | |
| 2. **Generate embeddings** | |
| ```bash | |
| python embeddings.py | |
| ``` | |
| Follow the prompts to specify your document folder path. | |
| 3. **Update database path** | |
| Ensure the `db_path` in `retriever.py` points to your FAISS database: | |
| ```python | |
| db_path = r"path/to/your/faiss_db" | |
| ``` | |
| ### Embedding Model | |
| The system uses OpenAI's `text-embedding-3-large` model. You can modify this in `embeddings.py`: | |
| ```python | |
| embeddings = OpenAIEmbeddings(model="text-embedding-3-large") | |
| ``` | |
| ## Running the Application | |
| 1. **Start the FastAPI server** | |
| ```bash | |
| uvicorn app:app --reload --host 0.0.0.0 --port 8000 | |
| ``` | |
| 2. **Access the chatbot** | |
| Open your browser and navigate to: | |
| ``` | |
| http://localhost:8000 | |
| ``` | |
| ## Usage | |
| ### Web Interface | |
| - Type your questions in the chat interface | |
| - The bot will search its knowledge base first | |
| - If needed, it will perform web searches for current information | |
| - References are provided for all responses | |
| ### API Endpoint | |
| Send POST requests to `/chat`: | |
| ```bash | |
| curl -X POST "http://localhost:8000/chat" \ | |
| -H "Content-Type: application/json" \ | |
| -d '{"query": "What is carbon neutrality?"}' | |
| ``` | |
| ### Supported Queries | |
| - Environmental policy questions | |
| - Carbon emission inquiries | |
| - Sustainability best practices | |
| - COP29-related information | |
| - General conversational queries | |
| ## Key Components | |
| ### Retriever Pipeline (`retriever.py`) | |
| - Manages the hybrid search strategy | |
| - Maintains conversation history | |
| - Handles query preprocessing and response formatting | |
| ### Document Processing (`file_loader.py`) | |
| - Supports multiple file formats | |
| - Preserves metadata during loading | |
| - Implements fallback loaders for reliability | |
| ### Vector Store (`embeddings.py`) | |
| - Creates and manages FAISS indexes | |
| - Handles document chunking with overlap | |
| - Preserves metadata through the embedding process | |
| ## Development | |
| ### Adding New Document Types | |
| Extend the `FILE_LOADER_MAPPING` in `file_loader.py`: | |
| ```python | |
| FILE_LOADER_MAPPING = { | |
| ".new_ext": (YourLoaderClass, {"param": "value"}), | |
| # ... existing mappings | |
| } | |
| ``` | |
| ### Customizing Responses | |
| Modify the `predefined_responses` dictionary in `retriever.py` to add custom responses for common queries. | |
| ### Adjusting Search Parameters | |
| Configure search behavior in `hybrid_chain()`: | |
| - `k=5`: Number of documents to retrieve | |
| - `chunk_size=1000`: Size of text chunks | |
| - `chunk_overlap=100`: Overlap between chunks | |
| ## Troubleshooting | |
| ### Common Issues | |
| 1. **FAISS Loading Errors** | |
| - Ensure `allow_dangerous_deserialization=True` is set | |
| - Check file permissions on the database directory | |
| 2. **API Key Issues** | |
| - Verify `.env` file is in the root directory | |
| - Check API key validity | |
| 3. **Memory Issues** | |
| - Reduce chunk size or number of retrieved documents | |
| - Consider using a smaller embedding model | |
| ### Debug Mode | |
| Enable detailed logging: | |
| ```python | |
| logging.basicConfig(level=logging.DEBUG) | |
| ``` | |
| ## Performance Optimization | |
| - **Async Processing**: FastAPI handles requests asynchronously | |
| - **Caching**: Consider implementing Redis for response caching | |
| - **Batch Processing**: Process multiple documents simultaneously | |
| - **Index Optimization**: Regularly rebuild FAISS indexes for optimal performance | |
| ## Contributing | |
| 1. Fork the repository | |
| 2. Create your feature branch (`git checkout -b feature/AmazingFeature`) | |
| 3. Commit your changes (`git commit -m 'Add some AmazingFeature'`) | |
| 4. Push to the branch (`git push origin feature/AmazingFeature`) | |
| 5. Open a Pull Request | |
| ## License | |
| This project is licensed under the MIT License - see the LICENSE file for details. | |
| ## Acknowledgments | |
| - Built with [LangChain](https://langchain.com/) for RAG capabilities | |
| - Powered by [OpenAI](https://openai.com/) for embeddings and language models | |
| - [FAISS](https://github.com/facebookresearch/faiss) for efficient similarity search | |
| - [FastAPI](https://fastapi.tiangolo.com/) for the web framework | |
| ## Contact | |
| For questions or support, please contact: info@carbonnote.ai | |
| ## Contributors | |
| 1. Elizabeth Osanyinro, University of Bradford, UK | |
| 2. Oluwole Fagbohun, Carbonnote, USA | |
| 3. Ernest Effiong Offiong, Carbonnote, USA | |
| 4. Maxwell Nwanna, RideNear, UK | |
| 5. Grace Farayola, University of Buckingham, UK | |
| 6. Olaitan Olaonipekun, Vuhosi Limited, UK | |
| 7. Abiola Oludotun, Readrly Limited, UK | |
| 8. Sayo Agunbiade, Independent Researcher, UK | |
| 9. Oladotun Fasogbon, Independent Researcher, UK | |
| 10. Ogheneruona Maria Esegbon-Isikeh, Readrly Limited, UK | |
| 11. Lanre Shittu, Independent Researcher, UK | |
| 12. Toyese Oloyede, Independent Researcher, UK | |
| 13. Sa'id Olanrewaju, Readrly Limited, UK | |
| 14. Christopher J Ozurumba, Independent Researcher, UK | |
| 15. Nelson Ogbeide, independent Researcher, UK | |
| **Note**: This is a beta version. For production use, please ensure proper security measures, rate limiting, and error handling are implemented. | |
| --- | |
| license: apache-2.0 | |
| --- | |